Search Results for author:
Found 11 papers, 0 papers with code
Algorithmic construction of Lyapunov functions for continuous vector fields via convex semi-infinite programs
This article presents a novel numerically tractable technique for synthesizing Lyapunov functions for equilibria of nonlinear vector fields.
A numerical algorithm for attaining the Chebyshev bound in optimal learning
For a hypothesis space realized as a compact but not necessarily convex subset of a finite-dimensional subspace of some underlying Banach space, this algorithm computes the Chebyshev radius and the Chebyshev center of the hypothesis space, thereby solving the problem of optimal recovery of functions from data.
Cross apprenticeship learning framework: Properties and solution approaches
Apprenticeship learning is a framework in which an agent learns a policy to perform a given task in an environment using example trajectories provided by an expert.
Implicit Function Theorem: Estimates on the size of the domain
In this article, we present explicit estimates of the size of the domain on which the Implicit Function Theorem and the Inverse Function Theorem are valid.
Novel min-max reformulations of Linear Inverse Problems
In this article, we dwell into the class of so-called ill-posed Linear Inverse Problems (LIP) which simply refers to the task of recovering the entire signal from its relatively few random linear measurements.
Reference tracking stochastic model predictive control over unreliable channels and bounded control actions
A stochastic model predictive control framework over unreliable Bernoulli communication channels, in the presence of unbounded process noise and under bounded control inputs, is presented for tracking a reference signal.
Optimization and Control
Dictionary Learning with Almost Sure Error Constraints
A dictionary is a database of standard vectors, so that other vectors / signals are expressed as linear combinations of dictionary vectors, and the task of learning a dictionary for a given data is to find a good dictionary so that the representation of data points has desirable features.
On Convex Duality in Linear Inverse Problems
In this article we dwell into the class of so called ill posed Linear Inverse Problems (LIP) in machine learning, which has become almost a classic in recent times.
Scenario approach for minmax optimization with emphasis on the nonconvex case: positive results and caveats
Moreover, we perform a detailed study of both the asymptotic behaviour (consistency) and finite time behaviour of the scenario approach in the more general setting of nonconvex minmax optimization problems.
A complete characterization of optimal dictionaries for least squares representation
Dictionaries are collections of vectors used for representations of elements in Euclidean spaces.
Optimal dictionary for least squares representation
Dictionaries are collections of vectors used for representations of random vectors in Euclidean spaces.
